Robust model-based analysis of single-particle tracking experiments with Spot-On
Robust model-based analysis of single-particle tracking experiments with Spot-On
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DOI：
10.7554/elife.33125
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发表时间：
2018-01-04
期刊：
影响因子：
7.7
通讯作者：
Darzacq, Xavier
中科院分区：
文献类型：
--
作者：
Hansen, Anders S.;Woringer, Maxime;Darzacq, Xavier
单粒子跟踪（SPT）技术可以直接观察活细胞中蛋白质的结合和扩散动力学，已成为连接生物化学和细胞生物学的重要方法。然而，由于数据分析和实验设计的偏差，从SPT研究中准确推断信息是具有挑战性的。为了解决分析偏差，我们引入了“Spot-On”，一个直观的网络界面。Spot-On实现了一个动力学建模框架，该框架可以解释已知的偏差，包括分子移动失焦，并从汇集的单分子轨迹中可靠地推断扩散常数和亚种群。为了最大限度地减少固有的实验偏差，我们实现并验证了频闪光激活SPT (spaSPT)，它最大限度地减少了运动模糊偏差和跟踪误差。我们使用实验逼真的模拟验证了Spot-On，并表明Spot-On优于其他方法。然后，我们将Spot-On应用于活体哺乳动物细胞的spaSPT数据，跨越广泛的核动力学，并证明Spot-On一致且稳健地推断亚群分数和扩散常数。
Single-particle tracking (SPT) has become an important method to bridge biochemistry and cell biology since it allows direct observation of protein binding and diffusion dynamics in live cells. However, accurately inferring information from SPT studies is challenging due to biases in both data analysis and experimental design. To address analysis bias, we introduce 'Spot-On', an intuitive web-interface. Spot-On implements a kinetic modeling framework that accounts for known biases, including molecules moving out-of-focus, and robustly infers diffusion constants and subpopulations from pooled single-molecule trajectories. To minimize inherent experimental biases, we implement and validate stroboscopic photo-activation SPT (spaSPT), which minimizes motion-blur bias and tracking errors. We validate Spot-On using experimentally realistic simulations and show that Spot-On outperforms other methods. We then apply Spot-On to spaSPT data from live mammalian cells spanning a wide range of nuclear dynamics and demonstrate that Spot-On consistently and robustly infers subpopulation fractions and diffusion constants.